Detecting domain-specific information needs in conversational search dialogues

As conversational search becomes more pervasive, it becomes increasingly important to understand the user's underlying needs when they converse with such systems in diverse contexts. We report on an insitu experiment to collect conversationally described information needs in a home cooking scenario. A human experimenter acted as the perfect conversational search system. Based on the transcription of the utterances, we present a preliminary coding scheme comprising 27 categories to annotate the information needs of users. Moreover, we use these annotations to perform prediction experiments based on random forest classification to establish the feasibility of predicting the information need from the raw utterances. We find that a reasonable accuracy in predicting information need categories is possible and evidence the importance of stopwords in the classfication task

Analysing the Impact of Machine Learning to Model Subjective Mental Workload: A Case Study in Third-Level Education

Mental workload measurement is a complex multidisciplinary research area that includes both the theoretical and practical development of models. These models are aimed at aggregating those factors, believed to shape mental workload, and their interaction, for the purpose of human performance prediction. In the literature, models are mainly theory-driven: their distinct development has been influenced by the beliefs and intuitions of individual scholars in the disciplines of Psychology and Human Factors. This work presents a novel research that aims at reversing this tendency. Specifically, it employs a selection of learning techniques, borrowed from machine learning, to induce models of mental workload from data, with no theoretical assumption or hypothesis. These models are subsequently compared against two well-known subjective measures of mental workload, namely the NASA Task Load Index and the Workload Profile. Findings show how these data-driven models are convergently valid and can explain overall perception of mental workload with a lower error

Explaining the influence of prior knowledge on POMCP policies

Partially Observable Monte Carlo Planning is a recently proposed online planning algorithm which makes use of Monte Carlo Tree Search to solve Partially Observable Monte Carlo Decision Processes. This solver is very successful because of its capability to scale to large uncertain environments, a very important property for current real-world planning problems. In this work we propose three main contributions related to POMCP usage and interpretability. First, we introduce a new planning problem related to mobile robot collision avoidance in paths with uncertain segment difficulties, and we show how POMCP performance in this context can take advantage of prior knowledge about segment difficulty relationships. This problem has direct real-world applications, such as, safety management in industrial environments where human-robot interaction is a crucial issue. Then, we present an experimental analysis about the relationships between prior knowledge provided to the algorithm and performance improvement, showing that in our case study prior knowledge affects two main properties, namely, the distance between the belief and the real state, and the mutual information between segment difficulty and action taken in the segment. This analysis aims to improve POMCP explainability, following the line of recently proposed eXplainable AI and, in particular, eXplainable planning. Finally, we analyze results on a synthetic case study and show how the proposed measures can improve the understanding about internal planning mechanisms

Why missing premises can be missed: Evaluating arguments by determining their lever

By taking an argument to consist of one premise and one conclusion, the Periodic Table of Arguments (PTA) excludes from its conceptualization the element traditionally called the ‘connecting premise’ or ‘warrant’ – which is often missing from the discourse. This paper answers the question of how to evaluate the underlying mechanism of an argument by presenting a method for formulating its ‘argumentative lever’ based on an identification of its type

A Novel Parabolic Model of Instructional Efficiency Grounded on Ideal Mental Workload and Performance

Instructional efficiency within education is a measurable concept and models have been proposed to assess it. The main assumption behind these models is that efficiency is the capacity to achieve established goals at the minimal expense of resources. This article challenges this assumption by contributing to the body of Knowledge with a novel model that is grounded on ideal mental workload and performance, namely the parabolic model of instructional efficiency. A comparative empirical investigation has been constructed to demonstrate the potential of this model for instructional design evaluation. Evidence demonstrated that this model achieved a good concurrent validity with the well-known likelihood model of instructional efficiency, treated as baseline, but a better discriminant validity for the evaluation of the training and learning phases. Additionally, the inferences produced by this novel model have led to a superior information gain when compared to the baseline

COGNITIVE LEARNING AND ROBOTICS: INNOVATIVE TEACHING FOR INCLUSIVITY

We present the interdisciplinary CoWriting Kazakh project in which a social robot acts as a peer in learning the new Kazakh Latin alphabet, to which Kazakhstan is going to shift from the current Kazakh Cyrillic by 2030. We discuss the past literature on cognitive learning and script acquisition in-depth and present a theoretical framing for this study. The results of word and letter analyses from two user studies conducted between 2019 and 2020 are presented. Learning the new alphabet through Kazakh words with two or more syllables and special native letters resulted in significant learning gains. These results suggest that reciprocal Cyrillic-to-Latin script learning results in considerable cognitive benefits due to mental conversion, word choice, and handwriting practices. Overall, this system enables school-age children to practice the new Kazakh Latin script in an engaging learning scenario. The proposed theoretical framework illuminates the understanding of teaching and learning within the multimodal robot-assisted script learning scenario and beyond its scope

Building Towards Automated Cyberbullying Detection: A Comparative Analysis

The increased use of social media between digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, it’s this anonymity feature which gives users freedom of speech and allows them to conduct activities without being judged by others can also encourage cyberbullying and hate speech. Predators can hide their identity and reach a wide range of audience anytime and anywhere. According to the detrimental effect of cyberbullying, there is a growing need for cyberbullying detection approaches. In this survey paper, a comparative analysis of the automated cyberbullying techniques from different perspectives is discussed including data annotation, data pre-processing and feature engineering. In addition, the importance of emojis in expressing emotions as well as their influence on sentiment classification and text comprehension lead us to discuss the role of incorporating emojis in the process of cyberbullying detection and their influence on the detection performance. Furthermore, the different domains for using Self-Supervised Learning (SSL) as an annotation technique for cyberbullying detection is explored

Maintaining Structured Experiences for Robots via Human Demonstrations: An Architecture To Convey Long-Term Robot\u2019s Beliefs

This PhD thesis presents an architecture for structuring experiences, learned through demonstrations, in a robot memory. To test our architecture, we consider a specific application where a robot learns how objects are spatially arranged in a tabletop scenario. We use this application as a mean to present a few software development guidelines for building architecture for similar scenarios, where a robot is able to interact with a user through a qualitative shared knowledge stored in its memory. In particular, the thesis proposes a novel technique for deploying ontologies in a robotic architecture based on semantic interfaces. To better support those interfaces, it also presents general-purpose tools especially designed for an iterative development process, which is suitable for Human-Robot Interaction scenarios. We considered ourselves at the beginning of the first iteration of the design process, and our objective was to build a flexible architecture through which evaluate different heuristic during further development iterations. Our architecture is based on a novel algorithm performing a oneshot structured learning based on logic formalism. We used a fuzzy ontology for dealing with uncertain environments, and we integrated the algorithm in the architecture based on a specific semantic interface. The algorithm is used for building experience graphs encoded in the robot\u2019s memory that can be used for recognising and associating situations after a knowledge bootstrapping phase. During this phase, a user is supposed to teach and supervise the beliefs of the robot through multimodal, not physical, interactions. We used the algorithm to implement a cognitive like memory involving the encoding, storing, retrieving, consolidating, and forgetting behaviours, and we showed that our flexible design pattern could be used for building architectures where contextualised memories are managed with different purposes, i.e. they contains representation of the same experience encoded with different semantics. The proposed architecture has the main purposes of generating and maintaining knowledge in memory, but it can be directly interfaced with perceiving and acting components if they provide, or require, symbolical knowledge. With the purposes of showing the type of data considered as inputs and outputs in our tests, this thesis also presents components to evaluate point clouds, engage dialogues, perform late data fusion and simulate the search of a target position. Nevertheless, our design pattern is not meant to be coupled only with those components, which indeed have a large room of improvement

Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition. Volume 2 : Traitement Automatique des Langues Naturelles

@ 6ème conférence conjointe: JEP-TALN-RECITAL 2020no abstrac